Open Source tools are an excellent choice for getting started with Machine learning. This article covers some of the top ML frameworks and tools.
Keras is an open source neural network library that is written in the Python language. It should be noted that it is capable of running on top of other frameworks/software libraries, such as Microsoft Cognitive Toolkit, TensorFlow, and Theano. For those who don’t know, a neural network is actually a computing system that is meant to imitate the neural activity of animal brains, and is a collection of nodes called “artificial neurons”.
Keras as a neural network helps to optimize a lot of functions, and is used to make working with image and text data infinitely easier. This could help to improve productivity for countless platforms and companies who work with data. It is a tool that caters to the recent technological trend of deep learning, where companies are using AI to optimize their companies and find out more information about their consumer base, such as predictive trends or overall consumer trends.
Keras has over 200,000 users already, and was recently the 10th most cited tool in the 2018 Nuggets 2018 software poll, which indicates that it is rising in popularity and relevancy in the tech sector. It ultimately helps many companies experiment faster with certain processes, as well.Hire Keras Specialists
Take an MNIST dataset, train the network using Keras (or another program), and then o visualize the features in the neurons at different layers o See how removing neurons at different parts of the network impacts performance — maybe combined with the first?
Introduction: 1. DDPM - Diffusion Models Beat GANs on Image Synthesis (Machine Learning Research Paper Explained) 2. Lil Log What are Diffusion Models? Papers: Denoising Diffusion Probabilistic Models  DENOISING DIFFUSION IMPLICIT MODELS On Fast Sampling of Diffusion Probabilistic Models Problem 1: what is the problem the two papers aim to solve, and why is this problem important or interesting? (5 points) Problem 2: 1) summarize the three methods, including high-level ideas as well as technical details: the relevant details that are important to focus on (e.g., if there’s a model, define it; if there is a theorem, state it and explain why it’s important, etc) 2) what are the major differences of the three methods? (15 points) Problem 3: implement DDPM  and test...